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Newbie question using R's mtcars dataset with anova() function. My question is how to use anova() to select the best (nested) model. Here's some example data:

> anova(lm(mpg~disp,mtcars),lm(mpg~disp+wt,mtcars),lm(mpg~disp+wt+am,mtcars))
Analysis of Variance Table

Model 1: mpg ~ disp
Model 2: mpg ~ disp + wt
Model 3: mpg ~ disp + wt + am
  Res.Df    RSS Df Sum of Sq      F   Pr(>F)   
1     30 317.16                                
2     29 246.68  1    70.476 8.0036 0.008535 **
3     28 246.56  1     0.126 0.0143 0.905548   
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
> anova(lm(mpg~disp,mtcars),lm(mpg~disp+wt,mtcars),lm(mpg~disp+wt+hp,mtcars))
Analysis of Variance Table

Model 1: mpg ~ disp
Model 2: mpg ~ disp + wt
Model 3: mpg ~ disp + wt + hp
  Res.Df    RSS Df Sum of Sq       F   Pr(>F)   
1     30 317.16                                 
2     29 246.68  1    70.476 10.1201 0.003571 **
3     28 194.99  1    51.692  7.4228 0.010971 * 
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

My understanding is anova() compares the reduction in the residual sum of squares to report a corresponding p-value for each nested model, where lower p-values means that nested model is more significantly different from the first model.

Question 1: Why is it that changing the 3rd regressor variable effects results from the 2nd nest model? That is, the p-value for disp+wt model changes from 0.008535 to 0.003571 going from the first to the second example. (does anova's model 2 analysis use data from model 3???)

Question 2: Since the 3rd model's Sum of Sq value is much lower in the first example (e.g. 0.126 versus 51.692), I'd expect the p-value to be lower as well, but it in fact increases (e.g. 0.905548 versus 0.010971). Why?

Question 3: Ultimately I'm trying to understand, given a dataset with a lot of regressors, how to use anova() to find the best model. Any general rules of thumb are appreciated.

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  • $\begingroup$ Taking the Coursera regression course? Is this part of the Assignment? $\endgroup$ – Antoni Parellada Sep 16 '15 at 18:03
  • $\begingroup$ Yes, I'm taking the course, and doing the project now. This is not a question asked in the course, but it will help me set a direction to tackle the project. The function anova() was briefly mentioned in the course, but not covered. So I'm trying to learn more about it. $\endgroup$ – user46688 Sep 16 '15 at 18:24
  • $\begingroup$ @user46688 ... and I took a stab at the last of your questions, by editing my response... $\endgroup$ – Antoni Parellada Sep 17 '15 at 3:38
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Question 1:

Regarding the difference in p-values (or F-values) for Model 2 in the first ANOVA compared to the second, there is no explanation apparent to me. They should indeed be the same, since we are comparing at that point Model 1 to Model 2 in both instances. It would be very useful to get further input about the reason behind, but in the absence of an explanation, it is probably advisable to only include two models in each ANOVA call. The values calculated "manually" (I presume the correct values) are consistent with the first ANOVA call in your post with an F = 8.2852.

Question 2:

You are comparing 51.692 and 0.126 BUT this bulkier difference in the second case is a good thing: What you should be paying attention to is the difference in the second ANOVA between 246.68 for Model 2 and 194.99 for Model 3, which is much more of a drop in RSS than what you observe in in the first ANOVA, a change from 246.68 to 246.56, tantamount to nothing. So by including the third regressor in the second ANOVA, you shave off a ton of residual (misfitted) information that is now captured, whereas you hardly made a dent in the first set of models.

Question 3:

The question is how to use ANOVA to find the best model, but I think the answer is clear by now - low p-value makes it a good bet that you should include the regressor under consideration. Yet, what is not clear at all is the process by which you move along a series of ANOVA tests. In other words, I know you know because I've taken the Coursera course, that at first you are acquainted with $R^2$, which you can calculate manually, and understand the idea here (among many other places); after the idea of overfitting is introduced, Adjusted-$R^2$ values are explained, shortly followed by, yes, ANOVA (F-test). All these and others, such as the AIC criterion are tests or criteria to select models or include / exclude regressors. For instance, AIC and BIC used for non-nested models, and ANOVA and Likelihood Ratio Tests for nested models (I'm quoting a comment by @f coppens in this post.

The challenge, though, is to come up with models that make sense to test (check this post). Say you have 5 independent variables, and you are doing linear regression, or OLS, you can include or exclude each variable in the any conceivable model for a total of $2^{10}=1,024$ models before considering interactions (remember that mtcars has 10 variables besides mpg). This has its own problems with collinearity for instance. You can resort to your knowledge of cars, or just run with raw computer power through all combinations either with forward or backward stepwise selection as very nicely explained in this document, using R code along the lines of:

fit.full <- lm(mpg ~ ., data = mtcars)    
for.aic <- step(lm(mpg ~ 1, data = mtcars), direction = "forward", 
                 scope = formula(fit.full), k = 2, trace = 0)               # forward AIC
for.bic <- step(lm(mpg ~ 1, data=mtcars), direction = "forward", 
                 scope = formula(fit.full), k = log(32), trace = 0)         # forward BIC
back.aic <- step(fit.full, direction = "backward", k = 2, trace = 0)        # backward AIC
back.bic <- step(fit.full, direction = "backward", k = log(32), trace = 0)  # backward BIC

(Adjusted_R.square <- data.frame("Method"=c("for.aic", "for.bic", "back.aic", "back.bic"), 
"Adj.r.square"=c(summary(for.aic)$adj.r.square, summary(for.bic)$adj.r.square, 
summary(back.aic)$adj.r.square, summary(back.bic)$adj.r.square)))
summary(back.bic)

to select the model with the highest adjusted $R^2$ (or you can also run ANOVA) with the idea of later looking into possible interactions, and running residuals diagnostics.

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  • $\begingroup$ Thanks so much. I understand more after reading your points. $\endgroup$ – user46688 Sep 16 '15 at 22:56
  • $\begingroup$ @user46688 Great! It feels good to help. Good luck with your course. Please consider voting up helpful answers. $\endgroup$ – Antoni Parellada Sep 16 '15 at 23:21
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Question 1: Note, that the anova commands you provided above are equivalent to giving anova() the full model. If you do the command:

anova(m3) # where m3 is lm(mpg~disp+wt+am,mtcars)
anova(m4) # where m4 is lm(mpg~disp+wt+hp,mtcars)

you will see that the anova is really telling you the significance of each variable in the model. I think you can only compare one nested model to a full model at a time. Incidentally, by testing model 1 against model 2 before, you should already know that you are going to include the first variable, so there is no need to test the model excluding this variable again. This leads to the question of "Which variable do I include first, then?" Model creation is really an art of sorts, but there is a function in the MASS library stepAIC() which allows you to select what could be the most important variables in terms of AIC or some other specified criteria. (Using forward, backward, or stepwise selection.)

Question 2: I believe this is answered above, but to be clear, these significance values are not for the nested model, but for the adding that parameter to the model.

Question 3: Once you get into data that has a very large amount of possible predictors, you are not going to want to use anova() unless you have a general idea of what you want to include. If you want to know the effect of one specific variable (or a specific set of variables in a specific order), it is a good idea to use anova. If you really have no idea and are testing more with more than 10-15 possible combinations of predictors, I would suggest using something like stepAIC. LASSO and ridge regression are other ways to select parameters beyond just trying out with anova one at a time by hand or using stepAIC to automate a similar process.

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  • $\begingroup$ Your point about anova() is well taken. Stepwise variable selection can pose problems; there are alternatives discussed here, including LASSO and ridge regression as you suggest, that are more likely to give reproducibly reliable results. $\endgroup$ – EdM Sep 16 '15 at 20:18
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For Question 3 I would like to add to the answer given by Antoni. It is better to use cross-validation which is a direct method to choose among various models in forward stepwise, backward stepwise or best subset instead of being confused among which to use. This will not require you to use ANOVA() at all. ANOVA is better to use when you are adding terms like interactions, polynomial terms, splines, etc.,

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